Risk factors associated with long-term sick leave following COVID-19

Disease and treatment 19. mar 2024 3 min Epidemiology Research Kim Daniel Jakobsen Written by Kristian Sjøgren

A new study shows that being female, having a high body mass index (BMI), having asthma and having depression increase the absolute risk of long-term sick-leave following COVID-19. A researcher says that the study can identify groups at high risk in connection with COVID-19, and the method can also identify high- risk groups for other diseases.

If you had COVID-19 in late 2020 or early 2021, the risk of being on long-term sick leave later was 4.5%.

If you were also a woman with high BMI and depression, the risk of long-term sick leave after COVID-19 increased by 10%. The same generally applied to people older than 35 years with high BMI and depression. Being female and older than 35 years and having chronic asthma were also associated with a high risk of long-term sick leave.

The research elucidates how groups in society differed in the risk of long-term sick leave following COVID-19 but also shows a method for determining how many diseases affect us differently.

“Remember that SARS-CoV-2 mutates constantly, so the fact that some people had more than 10% risk of long-term sick leave following COVID-19 in 2020 does not mean that this applies today. These groups probably still have increased risk compared with the rest of the population, but the absolute risk may differ. We also show how to identify population groups at higher risk, which may warrant early investigation in future epidemics or in connection with other major diseases such as cancer or diseases related to pregnancy,” explains a researcher involved in the study, Kim Daniel Jakobsen, Epidemiology Research, Statens Serum Institut, Copenhagen, Denmark.

The research has been published in Communications Medicine.

Data on almost 90,000 people

When the COVID-19 pandemic exploded around the world, Statens Serum Institut in Denmark decided to collect data on people with COVID-19 using its EFTER-COVID questionnaire to monitor the health of Denmark’s population during the pandemic, especially long COVID (post-COVID-19 condition).

The questionnaires were sent to more than 800,000 people in Denmark who had a first positive RT-PCR test or a negative RT-PCR test.

These questionnaires asked about the COVID-19 illness trajectory and whether the respondents were on sick leave after COVID-19.

The researchers had access to basic information such as sex, age, BMI, level of education, depression, anxiety and other diseases, including type 2 diabetes and asthma.

This enabled the researchers to investigate the proportion of the respondents with COVID-19 with long-term sick leave, defined as sick leave exceeding one month more than one month after the initial RT-PCR test, and whether some population groups were especially vulnerable.

The researchers used data from the 88,818 respondents aged 15–65 years: 37,482 had a positive RT-PCR test and the rest a negative result and acted as controls.

“We expected a critical period when COVID-19 directly caused sick leave but most people also recovered within one month. We investigated whether special population groups had a higher risk of long-term sick leave in the following months because of long COVID,” says Kim Daniel Jakobsen.

Population groups at higher risk

The study shows that 1.4% of the people with an initial negative RT-PCR test had long-term sick leave.

That figure increased to 4.5% among people with a positive RT-PCR test, an increase of 3.1 percentage points.

According to Kim Daniel Jakobsen, this result is not surprising since it is in accordance with other analyses from Statens Serum Institut.

“But the percentage is still high, and society pays a high price if 3% of the people with COVID-19 cannot work for several months,” he adds.

The further analysis showed that some population groups were especially hard hit if they got COVID-19.

Some population groups had almost no additional risk of long-term sick leave, other groups exceeded 10% additional risk such as females with high BMI and depression and people 35–46 years old. The additional risk was 9% for people older than 35 years with high BMI and depression and 8% for women older than 35 years with chronic asthma.

High BMI was defined as ≥30 for people older than 18 years. For people younger than 18 years, the researchers used international limit values for obesity based on age and sex.

“The result is interesting because it identifies some population groups with a very high risk of long-term sick leave. It may also show that we need to improve the consideration of people’s differences in examining the effects of disease,” explains Kim Daniel Jakobsen.

Method is inherently interesting

According to Kim Daniel Jakobsen, the method used is inherently interesting, using machine learning and artificial intelligence to identify population groups with a higher risk of long COVID.

This method can also be used to learn more about other diseases.

“We could ideally use this method in future outbreaks of disease to rapidly identify whether some population groups have extra high risk because of underlying characteristics. As new methods are developed to identify population heterogeneity in the risk of disease, we must also add them to our repertoire. This will enable disease interventions to be investigated and targeted better,” concludes Kim Daniel Jakobsen.

Statens Serum Institut has a long tradition of epidemiology research, and today the epidemiology research milieu is among the strongest and most renow...

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